{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\ondre\OneDrive\Plocha\RnP\Revisions\Dataverse\svr_jeps_replication_log1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Jan 2023, 13:18:14

{com}. **************************************************************

. 
. *** Replication code for "The 'Commitment trap' Revisited: ***

. 
. *** Experimental Evidence on Ambiguous Nuclear Threats" by ***

. 
. *** Michal Smetana, Marek Vranka, and Ondrej Rosendorf     ***

. 
. **************************************************************

. 
. 
. 
. *** The code was written in Stata 17.0 BE-Basic Edition ***

. 
. 
. 
. *** Please reach out to ondrej.rosendorf@fsv.cuni.cz if you have any questions concerning this replication file ***

. 
. 
. 
. *** IMPORTANT: This file is accompanied by the svr_jeps_replication_data1 dataset ***

. 
. 
. 
. *** Before proceeding with the replication, please make sure that the "asdoc", "coefplot", "estout" and "catplot" package is installed ***

. 
. 
. 
. *** To install the asdoc package, use the following command ***

. 
. 
. 
. ssc install asdoc, replace
{txt}checking {hilite:asdoc} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** To install the coefplot package, use the following command ***

. 
. 
. 
. ssc install coefplot, replace
{txt}checking {hilite:coefplot} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** To install the estout package, use the following command ***

. 
. 
. 
. ssc install estout, replace
{txt}checking {hilite:estout} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** To install the catplot package, use the following command ***

. 
. 
. 
. ssc install catplot, replace
{txt}checking {hilite:catplot} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** Setting the output scheme to black and white ***

. 
. 
. 
. set scheme s1mono

. 
. 
. 
. ***************************************************

. 
. *** Replication of the results in the main text ***

. 
. ***************************************************

. 
. 
. 
. *** Figure 4 (coefficient plot) - Approval (DV), non-nuclear response subset ***

. 
. 
. 
. * Running the ordinal logit model (Model 1)

. 
. * Note that the "ib1" prefix serves to change the reference level for the scenario_n variable to the ambiguous threat treatment

. 
. * The "if response==0" condition filters out respondents who received the nuclear response scenario

. 
. ologit approval_ordinal i.ib1.scenario_n if response==0

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-818.56133}  
Iteration 1:{space 3}log likelihood = {res:-810.29945}  
Iteration 2:{space 3}log likelihood = {res:-810.28054}  
Iteration 3:{space 3}log likelihood = {res:-810.28053}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:489}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:16.56}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0003}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-810.28053}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0101}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}0  {c |}{col 15}{res}{space 2} .0728486{col 27}{space 2} .1956566{col 38}{space 1}    0.37{col 47}{space 3}0.710{col 55}{space 4}-.3106313{col 68}{space 3} .4563285
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.6733963{col 27}{space 2} .2009117{col 38}{space 1}   -3.35{col 47}{space 3}0.001{col 55}{space 4}-1.067176{col 68}{space 3}-.2796167
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.226531{col 27}{space 2} .3588165{col 55}{space 4}-4.929799{col 68}{space 3}-3.523264
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.933107{col 27}{space 2} .2227553{col 55}{space 4}  -3.3697{col 68}{space 3}-2.496515
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.749938{col 27}{space 2} .1680484{col 55}{space 4}-2.079307{col 68}{space 3}-1.420569
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.9112738{col 27}{space 2} .1501027{col 55}{space 4} -1.20547{col 68}{space 3}-.6170778
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .2808776{col 27}{space 2} .1435514{col 55}{space 4}-.0004779{col 68}{space 3} .5622332
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}  2.24251{col 27}{space 2} .1954322{col 55}{space 4}  1.85947{col 68}{space 3}  2.62555
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 1)

. 
. estimates store M1

. 
. 
. 
. * Running the ordinal logit model with controls (Model 2)

. 
. * Note that the "ib1" prefix serves to change the reference level for the scenario_n variable to the ambiguous threat treatment

. 
. * The "if response==0" condition filters out respondents who received the nuclear response scenario

. 
. ologit approval_ordinal i.ib1.scenario_n i.male c.age c.income i.education_bin i.party if response==0

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-797.69067}  
Iteration 1:{space 3}log likelihood = {res:-778.96749}  
Iteration 2:{space 3}log likelihood = {res:-778.86632}  
Iteration 3:{space 3}log likelihood = {res:-778.86628}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:476}
{txt}{col 57}{lalign 13:LR chi2({res:8})}{col 70} = {res}{ralign 6:37.65}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-778.86628}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0236}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}0  {c |}{col 15}{res}{space 2} .0973282{col 27}{space 2}  .200318{col 38}{space 1}    0.49{col 47}{space 3}0.627{col 55}{space 4}-.2952879{col 68}{space 3} .4899443
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.6540783{col 27}{space 2} .2059035{col 38}{space 1}   -3.18{col 47}{space 3}0.001{col 55}{space 4}-1.057642{col 68}{space 3}-.2505148
{txt}{space 13} {c |}
{space 7}1.male {c |}{col 15}{res}{space 2} .3320667{col 27}{space 2} .1668591{col 38}{space 1}    1.99{col 47}{space 3}0.047{col 55}{space 4} .0050289{col 68}{space 3} .6591044
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0206506{col 27}{space 2} .0062134{col 38}{space 1}    3.32{col 47}{space 3}0.001{col 55}{space 4} .0084725{col 68}{space 3} .0328287
{txt}{space 7}income {c |}{col 15}{res}{space 2}-.0216036{col 27}{space 2} .0236384{col 38}{space 1}   -0.91{col 47}{space 3}0.361{col 55}{space 4} -.067934{col 68}{space 3} .0247268
{txt}1.education~n {c |}{col 15}{res}{space 2} -.282203{col 27}{space 2} .1718737{col 38}{space 1}   -1.64{col 47}{space 3}0.101{col 55}{space 4}-.6190693{col 68}{space 3} .0546633
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .0858617{col 27}{space 2} .2087114{col 38}{space 1}    0.41{col 47}{space 3}0.681{col 55}{space 4}-.3232052{col 68}{space 3} .4949285
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.3432577{col 27}{space 2}   .20301{col 38}{space 1}   -1.69{col 47}{space 3}0.091{col 55}{space 4}  -.74115{col 68}{space 3} .0546345
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -3.71945{col 27}{space 2} .4887701{col 55}{space 4}-4.677422{col 68}{space 3}-2.761478
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -2.41909{col 27}{space 2} .3992949{col 55}{space 4}-3.201693{col 68}{space 3}-1.636486
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.232951{col 27}{space 2} .3709444{col 55}{space 4}-1.959988{col 68}{space 3} -.505913
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.4007418{col 27}{space 2} .3634354{col 55}{space 4}-1.113062{col 68}{space 3} .3115786
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .8328148{col 27}{space 2} .3648871{col 55}{space 4} .1176492{col 68}{space 3} 1.547981
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}  2.84907{col 27}{space 2}  .395036{col 55}{space 4} 2.074813{col 68}{space 3} 3.623326
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 2)

. 
. estimates store M2

. 
. 
. 
. * Generating the coefficient plot (Figure 4)

. 
. coefplot M1, bylabel(Model 1) || M2, bylabel (Model 2) ||, xline(0) coeflabels(0.scenario_n = "{c -(}bf:Treatment (control – ambiguity){c )-}" 2.scenario_n = "{c -(}bf:Treatment (explicit – ambiguity){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Figure 4)

. 
. graph export F04.png
{txt}{p 0 4 2}
file {bf}
F04.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Figure 5 (coefficient plot) - Approval (DV), ambiguous threat subset ***

. 
. 
. 
. * Running the ordinal logit model (Model 3)

. 
. * The "if scenario_n==1" condition filters out respondents who did not receive the ambiguous threat treatment

. 
. ologit approval_ordinal i.response if scenario_n==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-604.82817}  
Iteration 1:{space 3}log likelihood = {res:-584.82964}  
Iteration 2:{space 3}log likelihood = {res:-584.68267}  
Iteration 3:{space 3}log likelihood = {res:-584.68255}  
Iteration 4:{space 3}log likelihood = {res:-584.68255}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:337}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:40.29}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-584.68255}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0333}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-1.266774{col 27}{space 2} .2039036{col 38}{space 1}   -6.21{col 47}{space 3}0.000{col 55}{space 4}-1.666418{col 68}{space 3}-.8671303
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.822441{col 27}{space 2} .2941445{col 55}{space 4}-4.398954{col 68}{space 3}-3.245929
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.577815{col 27}{space 2} .2076777{col 55}{space 4}-2.984856{col 68}{space 3}-2.170774
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.584938{col 27}{space 2} .1715605{col 55}{space 4} -1.92119{col 68}{space 3}-1.248685
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.9224341{col 27}{space 2} .1554013{col 55}{space 4}-1.227015{col 68}{space 3} -.617853
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .2412945{col 27}{space 2}  .146144{col 55}{space 4}-.0451424{col 68}{space 3} .5277313
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}  2.24478{col 27}{space 2} .2361536{col 55}{space 4} 1.781928{col 68}{space 3} 2.707633
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 3)

. 
. estimates store M3

. 
. 
. 
. * Running the ordinal logit model with controls (Model 4)

. 
. * The "if scenario_n==1" condition filters out respondents who did not receive the ambiguous threat treatment

. 
. ologit approval_ordinal i.response i.male c.age c.income i.education_bin i.party if scenario_n==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-596.35575}  
Iteration 1:{space 3}log likelihood = {res: -569.0235}  
Iteration 2:{space 3}log likelihood = {res:-568.75179}  
Iteration 3:{space 3}log likelihood = {res:-568.75149}  
Iteration 4:{space 3}log likelihood = {res:-568.75149}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:332}
{txt}{col 57}{lalign 13:LR chi2({res:7})}{col 70} = {res}{ralign 6:55.21}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-568.75149}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0463}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-1.210365{col 27}{space 2} .2062394{col 38}{space 1}   -5.87{col 47}{space 3}0.000{col 55}{space 4}-1.614587{col 68}{space 3} -.806143
{txt}{space 7}1.male {c |}{col 15}{res}{space 2} -.095966{col 27}{space 2} .1985852{col 38}{space 1}   -0.48{col 47}{space 3}0.629{col 55}{space 4}-.4851857{col 68}{space 3} .2932538
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0138545{col 27}{space 2} .0074035{col 38}{space 1}    1.87{col 47}{space 3}0.061{col 55}{space 4} -.000656{col 68}{space 3}  .028365
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0541697{col 27}{space 2}  .029604{col 38}{space 1}    1.83{col 47}{space 3}0.067{col 55}{space 4}-.0038531{col 68}{space 3} .1121925
{txt}1.education~n {c |}{col 15}{res}{space 2}-.1669908{col 27}{space 2} .2026724{col 38}{space 1}   -0.82{col 47}{space 3}0.410{col 55}{space 4}-.5642215{col 68}{space 3} .2302398
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.5825393{col 27}{space 2} .2467946{col 38}{space 1}   -2.36{col 47}{space 3}0.018{col 55}{space 4}-1.066248{col 68}{space 3}-.0988308
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.5486903{col 27}{space 2} .2418891{col 38}{space 1}   -2.27{col 47}{space 3}0.023{col 55}{space 4}-1.022784{col 68}{space 3}-.0745964
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.421638{col 27}{space 2}   .48433{col 55}{space 4}-4.370907{col 68}{space 3}-2.472368
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.167273{col 27}{space 2} .4338573{col 55}{space 4}-3.017617{col 68}{space 3}-1.316928
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} -1.20584{col 27}{space 2} .4152288{col 55}{space 4}-2.019673{col 68}{space 3} -.392006
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.5085009{col 27}{space 2} .4102997{col 55}{space 4}-1.312674{col 68}{space 3} .2956718
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .7083795{col 27}{space 2} .4137656{col 55}{space 4}-.1025862{col 68}{space 3} 1.519345
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 2.745219{col 27}{space 2} .4584164{col 55}{space 4} 1.846739{col 68}{space 3} 3.643698
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates

. 
. estimates store M4

. 
. 
. 
. * Generating the coefficient plot (Figure 5)

. 
. coefplot M3, bylabel(Model 3) || M4, bylabel (Model 4) ||, xline(0) coeflabels(1.response = "{c -(}bf:Response (nuclear – non-nuclear){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Figure 5)

. 
. graph export F05.png
{txt}{p 0 4 2}
file {bf}
F05.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Figure 6 (coefficient plot) - Preference (DV), no subset ***

. 
. 
. 
. * Running the ordinal logit model (Model 5)

. 
. ologit preference_ordinal i.scenario_n

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1078.5522}  
Iteration 1:{space 3}log likelihood = {res:-1078.0361}  
Iteration 2:{space 3}log likelihood = {res:-1078.0361}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,001}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:1.03}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.5968}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-1078.0361}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0005}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}preference_~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1459436{col 27}{space 2} .1461635{col 38}{space 1}   -1.00{col 47}{space 3}0.318{col 55}{space 4}-.4324189{col 68}{space 3} .1405317
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.0965794{col 27}{space 2} .1472583{col 38}{space 1}   -0.66{col 47}{space 3}0.512{col 55}{space 4}-.3852004{col 68}{space 3} .1920415
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -.086703{col 27}{space 2} .1050101{col 55}{space 4}-.2925189{col 68}{space 3} .1191129
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} 1.640606{col 27}{space 2} .1205542{col 55}{space 4} 1.404324{col 68}{space 3} 1.876888
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 3.364007{col 27}{space 2} .2000507{col 55}{space 4} 2.971915{col 68}{space 3} 3.756099
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 5)

. 
. estimates store M5

. 
. 
. 
. * Running the ordinal logit model with controls (Model 6)

. 
. * Note that the "##" operator specifies that the model should include an interaction between scenario_n and response

. 
. ologit preference_ordinal i.scenario_n##i.response i.male c.age c.income i.education_bin i.party

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1055.7829}  
Iteration 1:{space 3}log likelihood = {res:-1019.3944}  
Iteration 2:{space 3}log likelihood = {res:-1019.2003}  
Iteration 3:{space 3}log likelihood = {res:-1019.2002}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:979}
{txt}{col 57}{lalign 13:LR chi2({res:11})}{col 70} = {res}{ralign 6:73.17}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-1019.2002}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0346}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}preference_~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.0560683{col 27}{space 2} .2236633{col 38}{space 1}   -0.25{col 47}{space 3}0.802{col 55}{space 4}-.4944403{col 68}{space 3} .3823037
{txt}{space 11}2  {c |}{col 15}{res}{space 2} .0198369{col 27}{space 2} .2270727{col 38}{space 1}    0.09{col 47}{space 3}0.930{col 55}{space 4}-.4252173{col 68}{space 3} .4648912
{txt}{space 13} {c |}
{space 3}1.response {c |}{col 15}{res}{space 2} .7052389{col 27}{space 2} .2154301{col 38}{space 1}    3.27{col 47}{space 3}0.001{col 55}{space 4} .2830037{col 68}{space 3} 1.127474
{txt}{space 13} {c |}
{space 3}scenario_n#{c |}
{space 5}response {c |}
{space 9}1 1  {c |}{col 15}{res}{space 2}-.2124486{col 27}{space 2} .3027478{col 38}{space 1}   -0.70{col 47}{space 3}0.483{col 55}{space 4}-.8058233{col 68}{space 3} .3809261
{txt}{space 9}2 1  {c |}{col 15}{res}{space 2}-.2811248{col 27}{space 2} .3058414{col 38}{space 1}   -0.92{col 47}{space 3}0.358{col 55}{space 4} -.880563{col 68}{space 3} .3183134
{txt}{space 13} {c |}
{space 7}1.male {c |}{col 15}{res}{space 2}-.3863028{col 27}{space 2} .1248933{col 38}{space 1}   -3.09{col 47}{space 3}0.002{col 55}{space 4}-.6310893{col 68}{space 3}-.1415163
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0146195{col 27}{space 2}  .004544{col 38}{space 1}   -3.22{col 47}{space 3}0.001{col 55}{space 4}-.0235256{col 68}{space 3}-.0057133
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0033273{col 27}{space 2} .0175359{col 38}{space 1}    0.19{col 47}{space 3}0.850{col 55}{space 4}-.0310424{col 68}{space 3} .0376969
{txt}1.education~n {c |}{col 15}{res}{space 2}  .038485{col 27}{space 2}  .130136{col 38}{space 1}    0.30{col 47}{space 3}0.767{col 55}{space 4}-.2165769{col 68}{space 3} .2935468
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.7808125{col 27}{space 2} .1562233{col 38}{space 1}   -5.00{col 47}{space 3}0.000{col 55}{space 4}-1.087004{col 68}{space 3}-.4746206
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.7349854{col 27}{space 2} .1510813{col 38}{space 1}   -4.86{col 47}{space 3}0.000{col 55}{space 4}-1.031099{col 68}{space 3}-.4388714
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-.9298786{col 27}{space 2}   .28851{col 55}{space 4}-1.495348{col 68}{space 3}-.3644094
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .8941205{col 27}{space 2} .2890527{col 55}{space 4} .3275876{col 68}{space 3} 1.460653
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 2.628172{col 27}{space 2} .3282805{col 55}{space 4} 1.984754{col 68}{space 3}  3.27159
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 6)

. 
. estimates store M6

. 
. 
. 
. * Generating the coefficient plot (Figure 6)

. 
. coefplot M5, bylabel(Model 5) || M6, bylabel (Model 6) ||, xline(0) coeflabels(1.scenario_n = "{c -(}bf:Treatment (ambiguity – control){c )-}" 2.scenario_n = "{c -(}bf:Treatment (explicit – control){c )-}" 1.response = "Response (nuclear – non-nuclear)" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)" 1.scenario_n#1.response = "Treatment (ambiguity) * Response (nuclear)" 2.scenario_n#1.response = "Treatment (explicit) * Response (nuclear)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Figure 6)

. 
. graph export F06.png
{txt}{p 0 4 2}
file {bf}
F06.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 1 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 1, Table 1 - Gender ***

. 
. 
. 
. * Generating a labelled version of the male variable

. 
. recode male (0=0 "Female") (1=1 "Male"), generate(male_label)
{txt}(0 differences between {bf:male} and {bf:male_label})

{com}. 
. 
. 
. * Obtaining summary statistics for the male variable

. 
. asdoc tab male_label, save(A01T01)

  {txt}RECODE of {c |}
male (male) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Female {c |}{res}        499       49.95       49.95
{txt}       Male {c |}{res}        500       50.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        999      100.00
Click to Open File:  {browse "A01T01.doc"}

{com}. 
. 
. 
. *** Appendix 1, Table 2 - Age ***

. 
. 
. 
. * Obtaining summary statistics for the age variable

. 
. asdoc sum age, save(A01T02)

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        997    36.86259    13.81003         18         84
Click to Open File:  {browse "A01T02.doc"}

{com}. 
. 
. 
. * Generating a labelled version of the education_bin variable

. 
. recode education_bin (0=0 "Less than Bachelor's degree") (1=1 "Bachelor's degree or higher"), generate(education_bin_label)
{txt}(0 differences between {bf:education_bin} and {bf:education_bin_label})

{com}. 
. 
. 
. * Obtaining summary statistics for the male variable

. 
. asdoc tab education_bin_label, save(A01T03)

    {txt}RECODE of education_bin {c |}
                (education) {c |}      Freq.     Percent        Cum.
{hline 28}{c +}{hline 35}
Less than Bachelor's degree {c |}{res}        453       45.99       45.99
{txt}Bachelor's degree or higher {c |}{res}        532       54.01      100.00
{txt}{hline 28}{c +}{hline 35}
                      Total {c |}{res}        985      100.00
Click to Open File:  {browse "A01T03.doc"}

{com}. 
. 
. 
. * Generating a labelled version of the income variable

. 
. recode income (1=1 "Less than $10000") (2=2 "$10000 to $15999") (3=3 "$16000 to $19999") (4=4 "$20000 to $29999") (5=5 "$30000 to $39999") (6=6 "$40000 to $49999") (7=7 "$50000 to $59999") (8=8 "$60000 to $69999") (9=9 "$70000 to $79999") (10=10 "$80000 to $89999") (11=11 "$90000 to $99999") (12=12 "$100000 to $149999") (13=13 "More than $150000"), generate(income_label)
{txt}(0 differences between {bf:income} and {bf:income_label})

{com}. 
. 
. 
. * Obtaining summary statistics for the income variable

. 
. asdoc tab income_label, save(A01T04)

  {txt}RECODE of income {c |}
          (income) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
  Less than $10000 {c |}{res}         66        6.70        6.70
{txt}  $10000 to $15999 {c |}{res}         57        5.79       12.49
{txt}  $16000 to $19999 {c |}{res}         26        2.64       15.13
{txt}  $20000 to $29999 {c |}{res}         74        7.51       22.64
{txt}  $30000 to $39999 {c |}{res}         89        9.04       31.68
{txt}  $40000 to $49999 {c |}{res}         92        9.34       41.02
{txt}  $50000 to $59999 {c |}{res}         96        9.75       50.76
{txt}  $60000 to $69999 {c |}{res}         85        8.63       59.39
{txt}  $70000 to $79999 {c |}{res}         77        7.82       67.21
{txt}  $80000 to $89999 {c |}{res}         50        5.08       72.28
{txt}  $90000 to $99999 {c |}{res}         61        6.19       78.48
{txt}$100000 to $149999 {c |}{res}        124       12.59       91.07
{txt} More than $150000 {c |}{res}         88        8.93      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        985      100.00
Click to Open File:  {browse "A01T04.doc"}

{com}. 
. 
. 
. * Generating a labelled version of the party variable

. 
. recode party (0=0 "Republican") (1=1 "Democrat") (2=2 "Independent"), generate(party_label)
{txt}(0 differences between {bf:party} and {bf:party_label})

{com}. 
. 
. 
. * Obtaining summary statistics for the party variable

. 
. asdoc tab party_label, save(A01T05)

  {txt}RECODE of {c |}
      party {c |}
    (party) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        327       33.10       33.10
{txt}   Democrat {c |}{res}        331       33.50       66.60
{txt}Independent {c |}{res}        330       33.40      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        988      100.00
Click to Open File:  {browse "A01T05.doc"}

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 3 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 3, Table 1 - ordinal logistic regression (Model 1 and 2) ***

. 
. 
. 
. * Generating a table with results (Appendix 3, Table 1)

. 
. esttab M1 M2 using A03T01.rtf, noeqlines eqlabels(none) eform nogaps se pr2 varlabels(0.scenario_n "Treatment (control - ambiguity)" 2.scenario_n "Treatment (explicit - ambiguity)" 1.male "Gender (male)" age "Age" income "Income" 1.education_bin "Education (university degree)" 1.party "Party (Democrat - Republican)" 2.party "Party (Independent - Republican)" _cons "Constant") drop(1.scenario_n 0.male 0.education_bin 0.party cut1 cut2 cut3 cut4 cut5 cut6) mtitle("Approval" "Approval") title(Table 1: Ordinal logistic regression of crisis handling approval) nonumbers mlabels("Model 1" "Model 2")
{res}{txt}(output written to {browse  `"A03T01.rtf"'})

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 4 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 4, Table 1 - ordinal logistic regression (Model 3 and 4) ***

. 
. 
. 
. * Generating a table with results (Appendix 4, Table 1)

. 
. esttab M3 M4 using A04T01.rtf, noeqlines eqlabels(none) eform nogaps se pr2 varlabels(1.response "Response (nuclear - non-nuclear)" 1.male "Gender (male)" age "Age" income "Income" 1.education_bin "Education (university degree)" 1.party "Party (Democrat - Republican)" 2.party "Party (Independent - Republican)" _cons "Constant") drop(0.response 0.male 0.education_bin 0.party cut1 cut2 cut3 cut4 cut5 cut6) mtitle("Approval" "Approval") title(Table 1: Ordinal logistic regression of crisis handling approval) nonumbers mlabels("Model 3" "Model 4")
{res}{txt}(output written to {browse  `"A04T01.rtf"'})

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 5 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 5, Table 1 - ordinal logistic regression results (Model 5 and 6) ***

. 
. 
. 
. * Generating a table with results (Appendix 5, Table 1)

. 
. esttab M5 M6 using A05T01.rtf, noeqlines eqlabels(none) eform nogaps se pr2 varlabels(1.scenario_n "Treatment (ambiguity - control)" 2.scenario_n "Treatment (explicit - control)" 1.response "Response (nuclear - non-nuclear)" 1.scenario_n#1.response "Treatment (ambiguity) * Response (nuclear)" 2.scenario_n#1.response "Treatment (explicit) * Response (nuclear)" 1.male "Gender (male)" age "Age" income "Income" 1.education_bin "Education (university degree)" 1.party "Party (Democrat - Republican)" 2.party "Party (Independent - Republican)" _cons "Constant") drop(0.scenario_n 0.response 0.male 0.education_bin 0.party cut1 cut2 cut3 0.scenario_n#0.response 0.scenario_n#1.response 1.scenario_n#0.response 2.scenario_n#0.response) mtitle("Preference" "Preference") title(Table 1: Ordinal logistic regression of nuclear use preference) nonumbers mlabels("Model 5" "Model 6")
{res}{txt}(output written to {browse  `"A05T01.rtf"'})

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 7 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 7, Figure 1 (coefficient plot) - Approval (DV), explicit threat subset ***

. 
. 
. 
. * Running the ordinal logit model (Model 7)

. 
. * The "if scenario_n==2" condition filters out respondents who did not receive the explicit threat treatment

. 
. ologit approval_ordinal i.response if scenario_n==2

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-614.02819}  
Iteration 1:{space 3}log likelihood = {res: -604.9695}  
Iteration 2:{space 3}log likelihood = {res:-604.94154}  
Iteration 3:{space 3}log likelihood = {res:-604.94153}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:329}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:18.17}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-604.94153}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0148}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-.8379639{col 27}{space 2} .1983969{col 38}{space 1}   -4.22{col 47}{space 3}0.000{col 55}{space 4}-1.226815{col 68}{space 3}-.4491131
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -2.83017{col 27}{space 2} .2307478{col 55}{space 4}-3.282427{col 68}{space 3}-2.377912
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -1.81022{col 27}{space 2} .1789745{col 55}{space 4}-2.161003{col 68}{space 3}-1.459437
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.9656334{col 27}{space 2}  .156668{col 55}{space 4}-1.272697{col 68}{space 3}-.6585696
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.2737677{col 27}{space 2} .1488544{col 55}{space 4}-.5655169{col 68}{space 3} .0179815
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .8628409{col 27}{space 2} .1579847{col 55}{space 4} .5531966{col 68}{space 3} 1.172485
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}  2.44302{col 27}{space 2} .2490494{col 55}{space 4} 1.954892{col 68}{space 3} 2.931148
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 7)

. 
. estimates store M7

. 
. 
. 
. * Running the ordinal logit model with controls (Model 8)

. 
. * The "if scenario_n==2" condition filters out respondents who did not receive the explicit threat treatment

. 
. ologit approval_ordinal i.response i.male c.age c.income i.education_bin i.party if scenario_n==2

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-601.81571}  
Iteration 1:{space 3}log likelihood = {res:-579.00695}  
Iteration 2:{space 3}log likelihood = {res:-578.79652}  
Iteration 3:{space 3}log likelihood = {res:-578.79636}  
Iteration 4:{space 3}log likelihood = {res:-578.79636}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:322}
{txt}{col 57}{lalign 13:LR chi2({res:7})}{col 70} = {res}{ralign 6:46.04}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-578.79636}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0382}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-.9098932{col 27}{space 2} .2035301{col 38}{space 1}   -4.47{col 47}{space 3}0.000{col 55}{space 4}-1.308805{col 68}{space 3}-.5109816
{txt}{space 7}1.male {c |}{col 15}{res}{space 2}-.2449823{col 27}{space 2} .2006934{col 38}{space 1}   -1.22{col 47}{space 3}0.222{col 55}{space 4}-.6383341{col 68}{space 3} .1483695
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0208091{col 27}{space 2} .0071446{col 38}{space 1}    2.91{col 47}{space 3}0.004{col 55}{space 4} .0068059{col 68}{space 3} .0348123
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0656103{col 27}{space 2} .0280099{col 38}{space 1}    2.34{col 47}{space 3}0.019{col 55}{space 4} .0107119{col 68}{space 3} .1205086
{txt}1.education~n {c |}{col 15}{res}{space 2}-.3299001{col 27}{space 2} .2075143{col 38}{space 1}   -1.59{col 47}{space 3}0.112{col 55}{space 4}-.7366206{col 68}{space 3} .0768204
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.6295012{col 27}{space 2} .2509782{col 38}{space 1}   -2.51{col 47}{space 3}0.012{col 55}{space 4}-1.121409{col 68}{space 3}-.1375931
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.8047303{col 27}{space 2} .2464581{col 38}{space 1}   -3.27{col 47}{space 3}0.001{col 55}{space 4}-1.287779{col 68}{space 3}-.3216813
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.465278{col 27}{space 2}  .434662{col 55}{space 4}  -3.3172{col 68}{space 3}-1.613356
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.436422{col 27}{space 2} .4132153{col 55}{space 4}-2.246309{col 68}{space 3}-.6265347
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.5496278{col 27}{space 2}  .405514{col 55}{space 4}-1.344421{col 68}{space 3}  .245165
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2} .1793238{col 27}{space 2} .4032731{col 55}{space 4} -.611077{col 68}{space 3} .9697246
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} 1.359605{col 27}{space 2} .4110068{col 55}{space 4} .5540468{col 68}{space 3} 2.165164
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 3.000919{col 27}{space 2} .4583064{col 55}{space 4} 2.102655{col 68}{space 3} 3.899183
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 8)

. 
. estimates store M8

. 
. 
. 
. * Generating the coefficient plot (Appendix 7, Figure 1)

. 
. coefplot M7, bylabel(Model 7) || M8, bylabel (Model 8) ||, xline(0) coeflabels(1.response = "{c -(}bf:Response (nuclear – non-nuclear){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Appendix 7, Figure 1)

. 
. graph export A07F01.png
{txt}{p 0 4 2}
file {bf}
A07F01.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 7, Figure 2 (catplot) - Approval across response, explicit threat subset ***

. 
. 
. 
. * Generating a simplified version of the approval_ordinal variable

. 
. gen approval_adjusted = 0

. 
. replace approval_adjusted = 1 if approval_ordinal == 4
{txt}(148 real changes made)

{com}. 
. replace approval_adjusted = 2 if approval_ordinal == 5
{txt}(255 real changes made)

{com}. 
. replace approval_adjusted = 2 if approval_ordinal == 6
{txt}(230 real changes made)

{com}. 
. replace approval_adjusted = 2 if approval_ordinal == 7
{txt}(67 real changes made)

{com}. 
. 
. 
. * Generating a simplified version of preference_ordinal

. 
. gen preference_adjusted = 0

. 
. replace preference_adjusted = 1 if preference_ordinal == 3
{txt}(121 real changes made)

{com}. 
. replace preference_adjusted = 1 if preference_ordinal == 4
{txt}(31 real changes made)

{com}. 
. 
. 
. * Generating a labelled version of the scenario_n variable

. 
. recode scenario_n (0=0 "Control") (1=1 "Ambiguous threat") (2=2 "Explicit threat"), generate(scenario_n_label)
{txt}(0 differences between {bf:scenario_n} and {bf:scenario_n_label})

{com}. 
. 
. 
. * Generating a labelled version of the response variable

. 
. recode response (0=0 "Conventional") (1=1 "Nuclear"), generate(response_label)
{txt}(0 differences between {bf:response} and {bf:response_label})

{com}. 
. 
. 
. * Generating a catplot for approval_adjusted over response

. 
. * The "if scenario_n==2" condition filters out respondents who did not receive the explicit threat treatment

. 
. catplot approval_adjusted response_label if scenario_n==2, percent (response_label) ytitle ("Percent of Respondents by Approval", size(small)) intensity(75) asyvars stack blabel(bar, pos(center) format(%4.0f) size(small)) legend(rows(1) stack size(small) order(1 "Disapprove" 2 "Neither approve nor disapprove" 3 "Approve") symplacement(center))
{res}
{com}. 
. 
. 
. * Exporting the catplot (Appendix 7, Figure 2)

. 
. graph export A07F02.png
{txt}{p 0 4 2}
file {bf}
A07F02.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. ************************************************

. 
. *** Replication of the results in Appendix 8 ***

. 
. ************************************************

. 
. 
. 
. *** Appendix 8, Figure 1 (catplot) - Approval across treatment, non-nuclear response subset ***

. 
. 
. 
. * Generating a catplot for approval_adjusted over scenario_n

. 
. * The "if response==O" condition filters out respondents who received the nuclear strike scenario

. 
. catplot approval_adjusted scenario_n_label  if response==0, percent (scenario_n_label) ytitle ("Percent of Respondents by Approval", size(small)) intensity(75) asyvars stack blabel(bar, pos(center) format(%4.0f) size(small)) legend(rows(1) stack size(small) order(1 "Disapprove" 2 "Neither approve nor disapprove" 3 "Approve") symplacement(center))
{res}
{com}. 
. 
. 
. * Exporting the catplot (Appendix 8, Figure 1)

. 
. graph export A08F01.png
{txt}{p 0 4 2}
file {bf}
A08F01.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 8, Figure 2 (catplot) - Approval across response, ambiguous threat subset ***

. 
. 
. 
. * Generating a catplot for approval_adjusted over response

. 
. * The "if scenario==1" condition filters out respondents who did not receive the ambiguous threat treatment

. 
. catplot approval_adjusted response_label if scenario_n==1, percent (response_label) ytitle ("Percent of Respondents by Approval", size(small)) intensity(75) asyvars stack blabel(bar, pos(center) format(%4.0f) size(small)) legend(rows(1) stack size(small) order(1 "Disapprove" 2 "Neither approve nor disapprove" 3 "Approve") symplacement(center))
{res}
{com}. 
. 
. 
. * Exporting the catplot (Appendix 8, Figure 2)

. 
. graph export A08F02.png
{txt}{p 0 4 2}
file {bf}
A08F02.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 8, Figure 3 (catplot) - Preference over treatment

. 
. 
. 
. * Generating a catplot for preference_adjusted over scenario_n

. 
. catplot preference_adjusted scenario_n_label, percent (scenario_n_label) ytitle ("Percent of Respondents by Preference", size(small)) intensity(75) asyvars stack blabel(bar, pos(center) format(%4.0f) size(small)) legend(rows(1) stack size(small) order(1 "Prefer conventional" 2 "Prefer nuclear") symplacement(center))
{res}
{com}. 
. 
. 
. * Exporting the catplot (Appendix 8, Figure 3)

. 
. graph export A08F03.png
{txt}{p 0 4 2}
file {bf}
A08F03.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 8, Figure 4 (catplot) - Preference over response

. 
. 
. 
. * Generating a catplot for preference_adjusted over response

. 
. catplot preference_adjusted response_label, percent (response_label) ytitle ("Percent of Respondents by Preference", size(small)) intensity(75) asyvars stack blabel(bar, pos(center) format(%4.0f) size(small)) legend(rows(1) stack size(small) order(1 "Prefer conventional" 2 "Prefer nuclear") symplacement(center))
{res}
{com}. 
. 
. 
. * Exporting the catplot (Appendix 8, Figure 4)

. 
. graph export A08F04.png
{txt}{p 0 4 2}
file {bf}
A08F04.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *************************************************

. 
. *** Replication of the results in Appendix 10 ***

. 
. *************************************************

. 
. 
. 
. *** Appendix 10, Figure 1 (coefficient plot) - Approval (DV), non-nuclear response subset, MC pass ***

. 
. 
. 
. * Running the ordinal logit model (Model 1)

. 
. * Note that the "ib1" prefix serves to change the reference level for the scenario_n variable to the ambiguous threat treatment

. 
. * The "if response==0 & mc_pass==1" condition filters out respondents who received the nuclear response scenario and who did not pass the manipulation check

. 
. ologit approval_ordinal i.ib1.scenario_n if response==0 & mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-738.47288}  
Iteration 1:{space 3}log likelihood = {res:  -730.692}  
Iteration 2:{space 3}log likelihood = {res:-730.67266}  
Iteration 3:{space 3}log likelihood = {res:-730.67266}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:446}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:15.60}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0004}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-730.67266}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0106}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}0  {c |}{col 15}{res}{space 2} .1626973{col 27}{space 2} .2048931{col 38}{space 1}    0.79{col 47}{space 3}0.427{col 55}{space 4}-.2388858{col 68}{space 3} .5642804
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.6343699{col 27}{space 2} .2099721{col 38}{space 1}   -3.02{col 47}{space 3}0.003{col 55}{space 4}-1.045908{col 68}{space 3}-.2228321
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.506115{col 27}{space 2} .4305916{col 55}{space 4}-5.350059{col 68}{space 3}-3.662171
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.943886{col 27}{space 2} .2355294{col 55}{space 4}-3.405515{col 68}{space 3}-2.482257
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.715205{col 27}{space 2} .1740353{col 55}{space 4}-2.056308{col 68}{space 3}-1.374102
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.9721823{col 27}{space 2}  .156603{col 55}{space 4}-1.279119{col 68}{space 3} -.665246
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .2344872{col 27}{space 2}  .148861{col 55}{space 4} -.057275{col 68}{space 3} .5262493
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 2.214304{col 27}{space 2} .2004644{col 55}{space 4} 1.821401{col 68}{space 3} 2.607207
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 1)

. 
. estimates store M1mc

. 
. 
. 
. * Running the ordinal logit model with controls (Model 2)

. 
. * Note that the "ib1" prefix serves to change the reference level for the scenario_n variable to the ambiguous threat treatment

. 
. * The "if response==0 & mc_pass==1" condition filters out respondents who received the nuclear response scenario and who did not pass the manipulation check

. 
. ologit approval_ordinal i.ib1.scenario_n i.male c.age c.income i.education_bin i.party if response==0 & mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-719.28996}  
Iteration 1:{space 3}log likelihood = {res:-701.60674}  
Iteration 2:{space 3}log likelihood = {res:-701.49911}  
Iteration 3:{space 3}log likelihood = {res:-701.49907}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:434}
{txt}{col 57}{lalign 13:LR chi2({res:8})}{col 70} = {res}{ralign 6:35.58}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-701.49907}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0247}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}0  {c |}{col 15}{res}{space 2} .1748901{col 27}{space 2} .2098046{col 38}{space 1}    0.83{col 47}{space 3}0.405{col 55}{space 4}-.2363193{col 68}{space 3} .5860995
{txt}{space 11}2  {c |}{col 15}{res}{space 2} -.633374{col 27}{space 2} .2143688{col 38}{space 1}   -2.95{col 47}{space 3}0.003{col 55}{space 4}-1.053529{col 68}{space 3}-.2132188
{txt}{space 13} {c |}
{space 7}1.male {c |}{col 15}{res}{space 2} .3143886{col 27}{space 2} .1746906{col 38}{space 1}    1.80{col 47}{space 3}0.072{col 55}{space 4}-.0279986{col 68}{space 3} .6567758
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0198941{col 27}{space 2} .0065025{col 38}{space 1}    3.06{col 47}{space 3}0.002{col 55}{space 4} .0071493{col 68}{space 3} .0326388
{txt}{space 7}income {c |}{col 15}{res}{space 2} -.019133{col 27}{space 2} .0249223{col 38}{space 1}   -0.77{col 47}{space 3}0.443{col 55}{space 4}-.0679799{col 68}{space 3} .0297139
{txt}1.education~n {c |}{col 15}{res}{space 2}-.3184934{col 27}{space 2} .1804862{col 38}{space 1}   -1.76{col 47}{space 3}0.078{col 55}{space 4}-.6722399{col 68}{space 3}  .035253
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .1879806{col 27}{space 2} .2210063{col 38}{space 1}    0.85{col 47}{space 3}0.395{col 55}{space 4}-.2451838{col 68}{space 3}  .621145
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.3183892{col 27}{space 2} .2130027{col 38}{space 1}   -1.49{col 47}{space 3}0.135{col 55}{space 4}-.7358669{col 68}{space 3} .0990885
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.997906{col 27}{space 2} .5571756{col 55}{space 4} -5.08995{col 68}{space 3}-2.905862
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.426703{col 27}{space 2} .4248916{col 55}{space 4}-3.259475{col 68}{space 3} -1.59393
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.193878{col 27}{space 2} .3940381{col 55}{space 4}-1.966178{col 68}{space 3}-.4215776
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.4531196{col 27}{space 2} .3871682{col 55}{space 4}-1.211955{col 68}{space 3} .3057161
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .7924713{col 27}{space 2} .3882551{col 55}{space 4} .0315053{col 68}{space 3} 1.553437
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 2.828849{col 27}{space 2} .4176469{col 55}{space 4} 2.010276{col 68}{space 3} 3.647422
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 2)

. 
. estimates store M2mc

. 
. 
. 
. * Generating the coefficient plot (Appendix 10, Figure 1)

. 
. coefplot M1mc, bylabel(Model 1) || M2mc, bylabel (Model 2) ||, xline(0) coeflabels(0.scenario_n = "{c -(}bf:Treatment (control – ambiguity){c )-}" 2.scenario_n = "{c -(}bf:Treatment (explicit – ambiguity){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Appendix 10, Figure 1)

. 
. graph export A10F01.png
{txt}{p 0 4 2}
file {bf}
A10F01.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 10, Figure 2 (coefficient plot) - Approval (DV), ambiguous threat subset, MC pass ***

. 
. 
. 
. * Running the ordinal logit model (Model 3)

. 
. * The "if scenario_n==1 & mc_pass==1" condition filters out respondents who did not receive the ambiguous threat treatment and who did not pass the manipulation check

. 
. ologit approval_ordinal i.response if scenario_n==1 & mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-555.39658}  
Iteration 1:{space 3}log likelihood = {res:-534.46373}  
Iteration 2:{space 3}log likelihood = {res:-534.28746}  
Iteration 3:{space 3}log likelihood = {res:-534.28728}  
Iteration 4:{space 3}log likelihood = {res:-534.28728}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:309}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:42.22}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-534.28728}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0380}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-1.360675{col 27}{space 2} .2146184{col 38}{space 1}   -6.34{col 47}{space 3}0.000{col 55}{space 4} -1.78132{col 68}{space 3}-.9400311
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.793283{col 27}{space 2} .2990806{col 55}{space 4} -4.37947{col 68}{space 3}-3.207096
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.608664{col 27}{space 2} .2171209{col 55}{space 4}-3.034213{col 68}{space 3}-2.183115
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.610964{col 27}{space 2} .1793917{col 55}{space 4}-1.962565{col 68}{space 3}-1.259363
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.9591414{col 27}{space 2} .1623409{col 55}{space 4}-1.277324{col 68}{space 3}-.6409591
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .1875029{col 27}{space 2} .1517089{col 55}{space 4}-.1098412{col 68}{space 3}  .484847
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 2.244156{col 27}{space 2} .2473673{col 55}{space 4} 1.759325{col 68}{space 3} 2.728987
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 3)

. 
. estimates store M3mc

. 
. 
. 
. * Running the ordinal logit model with controls (Model 4)

. 
. * The "if scenario_n==1 & mc_pass==1" condition filters out respondents who did not receive the ambiguous threat treatment and who did not pass the manipulation check

. 
. ologit approval_ordinal i.response i.male c.age c.income i.education_bin i.party if scenario_n==1 & mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-546.93617}  
Iteration 1:{space 3}log likelihood = {res:-518.70691}  
Iteration 2:{space 3}log likelihood = {res:-518.37535}  
Iteration 3:{space 3}log likelihood = {res: -518.3749}  
Iteration 4:{space 3}log likelihood = {res: -518.3749}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:304}
{txt}{col 57}{lalign 13:LR chi2({res:7})}{col 70} = {res}{ralign 6:57.12}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-518.3749}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0522}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.response {c |}{col 15}{res}{space 2}-1.293443{col 27}{space 2} .2186301{col 38}{space 1}   -5.92{col 47}{space 3}0.000{col 55}{space 4} -1.72195{col 68}{space 3}-.8649355
{txt}{space 7}1.male {c |}{col 15}{res}{space 2}-.1073947{col 27}{space 2} .2082014{col 38}{space 1}   -0.52{col 47}{space 3}0.606{col 55}{space 4}-.5154618{col 68}{space 3} .3006725
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0150665{col 27}{space 2} .0078675{col 38}{space 1}    1.92{col 47}{space 3}0.055{col 55}{space 4}-.0003536{col 68}{space 3} .0304866
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0613298{col 27}{space 2} .0308324{col 38}{space 1}    1.99{col 47}{space 3}0.047{col 55}{space 4} .0008994{col 68}{space 3} .1217603
{txt}1.education~n {c |}{col 15}{res}{space 2}-.1341795{col 27}{space 2} .2122009{col 38}{space 1}   -0.63{col 47}{space 3}0.527{col 55}{space 4}-.5500857{col 68}{space 3} .2817266
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.5457717{col 27}{space 2} .2594061{col 38}{space 1}   -2.10{col 47}{space 3}0.035{col 55}{space 4}-1.054198{col 68}{space 3}-.0373451
{txt}{space 11}2  {c |}{col 15}{res}{space 2} -.571697{col 27}{space 2} .2528333{col 38}{space 1}   -2.26{col 47}{space 3}0.024{col 55}{space 4}-1.067241{col 68}{space 3} -.076153
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.280504{col 27}{space 2} .5104542{col 55}{space 4}-4.280975{col 68}{space 3}-2.280032
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.084595{col 27}{space 2} .4630668{col 55}{space 4} -2.99219{col 68}{space 3}-1.177001
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.119438{col 27}{space 2} .4433088{col 55}{space 4}-1.988308{col 68}{space 3}-.2505691
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.4284204{col 27}{space 2} .4384763{col 55}{space 4}-1.287818{col 68}{space 3} .4309773
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}  .776191{col 27}{space 2} .4434459{col 55}{space 4} -.092947{col 68}{space 3} 1.645329
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}  2.86932{col 27}{space 2} .4912853{col 55}{space 4} 1.906419{col 68}{space 3} 3.832222
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 4)

. 
. estimates store M4mc

. 
. 
. 
. * Generating the coefficient plot (Appendix 10, Figure 2)

. 
. coefplot M3mc, bylabel(Model 3) || M4mc, bylabel (Model 4) ||, xline(0) coeflabels(1.response = "{c -(}bf:Response (nuclear – non-nuclear){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Appendix 10, Figure 2)

. 
. graph export A10F02.png
{txt}{p 0 4 2}
file {bf}
A10F02.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 10, Figure 3 (coefficient plot) - Preference (DV), no subset, MC pass ***

. 
. 
. 
. * Running the ordinal logit model (Model 5)

. 
. * The "if mc_pass==1" condition filters out respondents who did not pass the manipulation check

. 
. ologit preference_ordinal i.scenario_n if mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-986.62637}  
Iteration 1:{space 3}log likelihood = {res: -986.0402}  
Iteration 2:{space 3}log likelihood = {res:-986.04015}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:922}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:1.17}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.5564}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-986.04015}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0006}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}preference_~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1551357{col 27}{space 2} .1530625{col 38}{space 1}   -1.01{col 47}{space 3}0.311{col 55}{space 4}-.4551326{col 68}{space 3} .1448613
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.1279759{col 27}{space 2} .1537081{col 38}{space 1}   -0.83{col 47}{space 3}0.405{col 55}{space 4}-.4292382{col 68}{space 3} .1732864
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-.0550557{col 27}{space 2} .1097427{col 55}{space 4}-.2701475{col 68}{space 3} .1600361
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}  1.64437{col 27}{space 2} .1261859{col 55}{space 4}  1.39705{col 68}{space 3} 1.891689
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 3.371186{col 27}{space 2} .2100835{col 55}{space 4}  2.95943{col 68}{space 3} 3.782942
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 5)

. 
. estimates store M5mc

. 
. 
. 
. * Running the ordinal logit model with controls (Model 6)

. 
. * The "if mc_pass==1" condition filters out respondents who did not pass the manipulation check

. 
. * Note that the "##" operator specifies that the model should include an interaction between scenario_n and response 

. 
. ologit preference_ordinal i.scenario_n##i.response i.male c.age c.income i.education_bin i.party if mc_pass==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -964.8826}  
Iteration 1:{space 3}log likelihood = {res:-927.63318}  
Iteration 2:{space 3}log likelihood = {res:-927.40534}  
Iteration 3:{space 3}log likelihood = {res:-927.40522}  
Iteration 4:{space 3}log likelihood = {res:-927.40522}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:901}
{txt}{col 57}{lalign 13:LR chi2({res:11})}{col 70} = {res}{ralign 6:74.95}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-927.40522}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0388}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}preference_~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}scenario_n {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .0232721{col 27}{space 2} .2386067{col 38}{space 1}    0.10{col 47}{space 3}0.922{col 55}{space 4}-.4443885{col 68}{space 3} .4909327
{txt}{space 11}2  {c |}{col 15}{res}{space 2} .0859021{col 27}{space 2} .2429739{col 38}{space 1}    0.35{col 47}{space 3}0.724{col 55}{space 4} -.390318{col 68}{space 3} .5621222
{txt}{space 13} {c |}
{space 3}1.response {c |}{col 15}{res}{space 2} .9054899{col 27}{space 2} .2286265{col 38}{space 1}    3.96{col 47}{space 3}0.000{col 55}{space 4} .4573902{col 68}{space 3}  1.35359
{txt}{space 13} {c |}
{space 3}scenario_n#{c |}
{space 5}response {c |}
{space 9}1 1  {c |}{col 15}{res}{space 2}-.3549112{col 27}{space 2} .3193332{col 38}{space 1}   -1.11{col 47}{space 3}0.266{col 55}{space 4}-.9807927{col 68}{space 3} .2709703
{txt}{space 9}2 1  {c |}{col 15}{res}{space 2}-.4577272{col 27}{space 2} .3218924{col 38}{space 1}   -1.42{col 47}{space 3}0.155{col 55}{space 4}-1.088625{col 68}{space 3} .1731702
{txt}{space 13} {c |}
{space 7}1.male {c |}{col 15}{res}{space 2}-.4385972{col 27}{space 2} .1308852{col 38}{space 1}   -3.35{col 47}{space 3}0.001{col 55}{space 4}-.6951275{col 68}{space 3}-.1820669
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0135536{col 27}{space 2} .0047963{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4}-.0229542{col 68}{space 3}-.0041529
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0126205{col 27}{space 2} .0183336{col 38}{space 1}    0.69{col 47}{space 3}0.491{col 55}{space 4}-.0233128{col 68}{space 3} .0485537
{txt}1.education~n {c |}{col 15}{res}{space 2} -.055799{col 27}{space 2} .1370381{col 38}{space 1}   -0.41{col 47}{space 3}0.684{col 55}{space 4}-.3243887{col 68}{space 3} .2127907
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.6780788{col 27}{space 2} .1624753{col 38}{space 1}   -4.17{col 47}{space 3}0.000{col 55}{space 4}-.9965245{col 68}{space 3} -.359633
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.7185665{col 27}{space 2} .1592384{col 38}{space 1}   -4.51{col 47}{space 3}0.000{col 55}{space 4}-1.030668{col 68}{space 3} -.406465
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-.7153561{col 27}{space 2} .3101662{col 55}{space 4}-1.323271{col 68}{space 3}-.1074416
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} 1.094497{col 27}{space 2} .3119387{col 55}{space 4} .4831083{col 68}{space 3} 1.705886
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 2.833693{col 27}{space 2} .3525076{col 55}{space 4}  2.14279{col 68}{space 3} 3.524595
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 6)

. 
. estimates store M6mc

. 
. 
. 
. * Generating the coefficient plot (Appendix 10, Figure 3)

. 
. coefplot M5mc, bylabel(Model 5) || M6mc, bylabel (Model 6) ||, xline(0) coeflabels(1.scenario_n = "{c -(}bf:Treatment (ambiguity – control){c )-}" 2.scenario_n = "{c -(}bf:Treatment (explicit – control){c )-}" 1.response = "Response (nuclear – non-nuclear)" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)" 1.scenario_n#1.response = "Treatment (ambiguity) * Response (nuclear)" 2.scenario_n#1.response = "Treatment (explicit) * Response (nuclear)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Appendix 10, Figure 3)

. 
. graph export A10F03.png
{txt}{p 0 4 2}
file {bf}
A10F03.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. ************************************************

. 
. *** Continue with svr_jeps_replication_code2 ***

. 
. ************************************************

. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\ondre\OneDrive\Plocha\RnP\Revisions\Dataverse\svr_jeps_replication_log1.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Jan 2023, 13:19:14
{txt}{.-}
{smcl}
{txt}{sf}{ul off}